Nearshore bathymetry is likely to be the coastal variable that most limits the investigation of coastal processes and the accuracy of numerical models in coastal areas, as acquiring medium spatial resolution data in the nearshore is highly demanding and costly. As such, the ability to derive bathymetry using remote sensing techniques is a topic of increasing interest in coastal monitoring and research. This contribution focuses on the application of the linear transform algorithm to obtain satellite-derived bathymetry (SDB) maps of the nearshore, at medium resolution (30 m), from freely available and easily accessible Landsat 8 imagery. The algorithm was tuned with available bathymetric Light Detection and Ranging (LiDAR) data for a 60-km-long nearshore stretch of a highly complex coastal system that includes barrier islands, exposed sandy beaches, and tidal inlets (Ria Formosa, Portugal). A comparison of the retrieved depths is presented, enabling the configuration of nearshore profiles and extracted isobaths to be explored and compared with traditional topographic/bathymetric techniques (e.g., high-and medium-resolution LiDAR data and survey-grade echo-sounding combined with high-precision positioning systems). The results demonstrate that the linear algorithm is efficient for retrieving bathymetry from multi-spectral satellite data for shallow water depths (0 to 12 m), showing a mean bias of −0.2 m, a median difference of −0.1 m, and a root mean square error of 0.89 m. Accuracy is shown to be depth dependent, an inherent limitation of passive optical detection systems. Accuracy further decreases in areas where turbidity is likely to be higher, such as locations adjacent to tidal inlets. The SDB maps provide reliable estimations of the shoreline position and of nearshore isobaths for different cases along the complex coastline analysed. The use of freely available satellite imagery proved to be a quick and reliable method for acquiring updated mediumresolution, high-frequency (days and weeks), low-cost bathymetric information for large areas and depths of up to 12 m in clear waters without wave breaking, allowing almost constant monitoring of the submerged beach and the shoreface.
The study aims to calibrate/validate and apply the dune-erosion model, XBeach, in order to predict morphological response to storm events along a meso-tidal, steeply sloping beach. More than 10,000 XBeach calibration runs, including different model parameters and erosion events, were compared with measurements of beach-profile response to storm conditions. Off-shore wave and tidal measurements were used as input for a SWAN wave model, which was used to provide wave conditions to XBeach. The results indicate that using XBeach to predict beachprofile morphodynamic response during storm events on steeply sloping intermediate-to-reflective beaches may be more demanding than for dissipative beaches and that the default model setup can overestimate dune/beach-face erosion. The performance of the model after calibration was satisfactory, with Brier Skill Scores from 0.2 to 0.72. XBeach was found to be more sensitive to input parameters such as the beach-face slope and the surf similarity parameter ξ (especially for values ξ>0.6). The calibrated XBeach setup was used for simulations of storm scenarios with different return periods (5, 25, and 50 years), and the simulations highlighted the fragility of the dune field and the potential for storm-induced dune retreat, lowering, and overwash in the study area. Finally, the nested SWAN/ XBeach models were forced by an existing operational wave-forecast WAVEWATCH-III/SWAN model, operated by the Portuguese Hydrographic Institute to generate daily forecasts of storm impact and serve as a prototype-case for an early warning system for storm hazard mitigation.
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